ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection

• URL: http://arxiv.org/abs/2607.27859v1
• Date: Thu, 30 Jul 2026 08:36:26 GMT
• Title: ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection
• Abstract summary: We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness.<n>A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem.<n>ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering.
• Score: 0.5689746155812007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering. The anomaly ensemble combines a one-class reconstruction model with an isolation forest and applies graduated rather than binary penalties. On 1,073 labeled malicious wallets covering 124,638 transactions, the ensemble achieves 0.923 +/- 0.013 ROC-AUC, compared with 0.891 +/- 0.016 for the reconstruction model alone, when the isolation forest is trained on benign wallets. Training it on the pooled population reverses its polarity and removes the ensemble gain. Controlled simulations reduce adversarial reward capture by 30-90 percent while legitimate-user scenarios change by 1-8 percent. Live campaigns recorded a 56 percent reduction in sybil allocation, a 49 percent increase in quality-wallet participation, and a 50 percent reduction in sell pressure.

Related papers

• HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition [0.0]
  Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous.<n>This study presents HB-PVI, a hierarchical Bayesian personalization and value-of-information framework.<n>It shows that value-of-information reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.
  arXiv  Detail & Related papers  (2026-09-04T15:38:07Z)
• Sense Once, Serve Many: Common-Trace Factorized Constrained PPO for Online Sensing-Session Consolidation in Multi-Tenant ISAC Networks [0.0]
  We propose Common-Trace Factorized Constrained Proximal Policy Optimization (CT-PPO)<n>CT-PPO achieves the highest mean macro return, exceeding matched Joint-Credit PPO (JC-PPO) by 0.934 and SLA-Aware Greedy by 1.847.<n>A four-way shows that the factorized surrogate alone yields no detectable macro-return gain, whereas adding common-trace reward credit produces the dominant improvement.
  arXiv  Detail & Related papers  (2026-08-29T13:20:21Z)
• Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization [58.14514930760722]
  We consider a decentralized setup in which the participants collaboratively train and serve a large neural network.<n>In this setup, we explore the possibility of unmaterializable weights, where a full weight set is never available to any one participant.<n>We introduce Unextractable Protocol Models (UPMs): a training and inference framework that leverages the sharded model setup.
  arXiv  Detail & Related papers  (2026-05-22T10:24:57Z)
• Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback [1.2362187555287152]
  Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors.<n>We propose a behavioral forecast-evaluation methodology that complements accuracy tests by assessing the intermediate decision process itself.
  arXiv  Detail & Related papers  (2026-05-07T06:31:34Z)
• Training Language Models for Bilateral Trade with Private Information [47.25254982913256]
  Bilateral trade demands individual, strategic surplus rationality, and cooperation to realize gains from trade.<n>We develop a structured bargaining environment where large language models negotiate via tool calls within an event-driven simulator.<n>The environment serves two purposes: a benchmark for frontier models and as a training environment for open-weight models via reinforcement learning.
  arXiv  Detail & Related papers  (2026-04-10T03:04:20Z)
• Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases [0.0]
  As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge.<n>We propose a deliberative curation protocol combining three underlying governance layers.<n>We evaluate the protocol through agent-based simulation with 100 agents across seven behavioral archetypes under two adversity scenarios.
  arXiv  Detail & Related papers  (2026-03-27T12:23:21Z)
• Quantifying Return on Security Controls in LLM Systems [0.0]
  This paper introduces a decision-oriented framework to quantify residual risk.<n>It converts adversarial probe outcomes into financial risk estimates and return-on-control metrics.
  arXiv  Detail & Related papers  (2025-12-17T04:58:09Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Federated Distributed Key Generation [0.0]
  We introduce Federated Distributed Key Generation (FDKG) that makes participation optional and trust heterogeneous.<n>FDKG completes both generation and reconstruction in a single broadcast round each.<n>Our analysis shows that (i) generation ensures correctness, privacy, and robustness under standard PVSS-based DKG assumptions.
  arXiv  Detail & Related papers  (2025-02-28T08:31:16Z)
• Simple Opinion Dynamics for No-Regret Learning [38.61048016579232]
  We study a cooperative multi-agent bandit setting in the distributed GOSSIP model. We introduce and analyze families of memoryless and time-independent protocols for this setting. For stationary reward settings, we prove for the first time that these simple protocols exhibit best-of-both-worlds behavior.
  arXiv  Detail & Related papers  (2023-06-14T17:59:15Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• Distributional Reinforcement Learning with Dual Expectile-Quantile Regression [51.87411935256015]
  quantile regression approach to distributional RL provides flexible and effective way of learning arbitrary return distributions.<n>We show that distributional estimation guarantees vanish, and we empirically observe that the estimated distribution rapidly collapses to its mean.<n>Motivated by the efficiency of $L$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning.
  arXiv  Detail & Related papers  (2023-05-26T12:30:05Z)
• Semi-supervised Contrastive Learning with Similarity Co-calibration [72.38187308270135]
  We propose a novel training strategy, termed as Semi-supervised Contrastive Learning (SsCL) SsCL combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning. We show that SsCL produces more discriminative representation and is beneficial to few shot learning.
  arXiv  Detail & Related papers  (2021-05-16T09:13:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.